Indexing the positions of continuously moving objects
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Mining asynchronous periodic patterns in time series data
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Partially Periodic Event Patterns with Unknown Periods
Proceedings of the 17th International Conference on Data Engineering
Identifying Representative Trends in Massive Time Series Data Sets Using Sketches
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Meta-patterns: Revealing Hidden Periodic Patterns
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Spatial queries in dynamic environments
ACM Transactions on Database Systems (TODS)
InfoMiner+: Mining Partial Periodic Patterns with Gap Penalties
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Mining Surprising Periodic Patterns
Data Mining and Knowledge Discovery
Prediction and indexing of moving objects with unknown motion patterns
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
STRIPES: an efficient index for predicted trajectories
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Mining, indexing, and querying historical spatiotemporal data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining periodic patterns with gap requirement from sequences
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Periodicity Detection in Time Series Databases
IEEE Transactions on Knowledge and Data Engineering
Summarizing itemset patterns: a profile-based approach
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Mining Frequent Spatio-Temporal Sequential Patterns
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
WARP: Time Warping for Periodicity Detection
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Summarizing itemset patterns using probabilistic models
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
The TPR*-tree: an optimized spatio-temporal access method for predictive queries
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Query and update efficient B+-tree based indexing of moving objects
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Mining Periodic Behavior in Dynamic Social Networks
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
A Hybrid Prediction Model for Moving Objects
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Mining periodic behaviors for moving objects
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Swarm: mining relaxed temporal moving object clusters
Proceedings of the VLDB Endowment
Mining user similarity based on routine activities
Information Sciences: an International Journal
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Periodicity is one of the most frequently occurring phenomena for moving objects. Animals usually have periodic movement behaviors, such as daily foraging behaviors or yearly migration behaviors. Such periodic behaviors are the keys to understand animal movement and they also reflect the seasonal, climate, or environmental changes of the ecosystem. However, periodic behaviors could be complicated, involving multiple interleaving periods, partial time span, and spatiotemporal noises and outliers. In this paper, we address the problem of mining periodic behaviors for moving objects. It involves two sub-problems: how to detect the periods in complex movements, and how to mine periodic behaviors. A period is usually a single value, such as 24 h. And a periodic behavior is a statistical description of the periodic movement for one specific period. For example, we could describe an animal's daily behavior in the way that "From 6 pm to 6 am, it has 90% probability staying at location A and from 7 am to 5 pm, it has 70% probability staying at location B and 30% probability staying at location C". So our tasks is to first detect the periods and then describe each periodic behavior according to different periods. Our main assumption is that the observed movement is generated from multiple interleaved periodic behaviors associated with certain reference locations. Based on this assumption, we propose a two-stage algorithm, Periodica, to solve the problem. At the first stage, the notion of reference spot is proposed to capture the reference locations. Through reference spots, multiple periods in the movement can be retrieved using a method that combines Fourier transform and autocorrelation. At the second stage, a probabilistic model is proposed to characterize the periodic behaviors. For a specific period, periodic behaviors are statistically generalized from partial movement sequences through hierarchical clustering. Finally, we show two extensions to the Periodica algorithm: (1) missing data interpolation, and (2) future movement prediction. Empirical studies on both synthetic and real data sets demonstrate the effectiveness of the proposed method.